polyfun | POLYgenic FUNctionally-informed fine

polyfun is a Python library. polyfun has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However polyfun build file is not available. You can download it from GitHub.

PolyFun (POLYgenic FUNctionally-informed fine-mapping) PolyLoc (POLYgenic LOCalization of complex trait heritability) PolyPred (POLYgenic Prediction of complex traits). This page contains the code of the methods PolyFun for functionally-informed fine-mapping, PolyLoc for polygenic localization of complex trait heritability, and PolyPred for complex trait prediction. PolyFun and PolyLoc are described in Weissbrod et al. 2020 Nat Genet. PolyPred is described in Weissbrod*, Kanai*, Shi* et al. 2021 medRxiv. PolyFun estimates prior causal probabilities for SNPs, which can then be used by fine-mapping methods like SuSiE or FINEMAP. Unlike previous methods for functionally-informed fine-mapping, PolyFun can aggregate polygenic data from across the entire genome and hundreds of functional annotations. PolyLoc generalizes fine-mapping by constructing minimal sets of SNPs that causally explain a given proportion (e.g. 50%) of SNP heritability. PolyPred exploits fine-mapping to improve trans-ethnic polygenic risk scores, by predicting using causal effect estimates intead of tagging effect estimates. We also provide a script called finemapper that facilitates fine-mapping with methods like SuSiE, saving many of the preprocessing steps often required to perform fine-mapping (e.g. handling allelic flips between the summary statistics and reference genotypes). The files in the ldstore directory are an adaptation of the ldstore package (written by Christian Benner) to Python 3.